Use the Climate Data Catalog#

Once we generate the catalog in the other notebook, we can use the catalog!

Imports#

import intake
from distributed import Client, LocalCluster
import hvplot.xarray
import matplotlib.pyplot as plt
import holoviews as hv
hv.extension("bokeh")

Spin up a Dask Cluster#

cluster = LocalCluster()
client = Client(cluster)
client

2022-07-21 09:18:38,603 - distributed.diskutils - INFO - Found stale lock file and directory '/Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-7e2ejztf', purging
2022-07-21 09:18:38,603 - distributed.diskutils - INFO - Found stale lock file and directory '/Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-enzfygne', purging
2022-07-21 09:18:38,604 - distributed.diskutils - INFO - Found stale lock file and directory '/Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-gdb25teo', purging
2022-07-21 09:18:38,604 - distributed.diskutils - INFO - Found stale lock file and directory '/Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-evnyv1jl', purging

Client

Client-0412cbb2-0900-11ed-9129-acde48001122

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

LocalCluster

0d31f132

Dashboard: http://127.0.0.1:8787/status	Workers: 4
Total threads: 12	Total memory: 16.00 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-61265159-e2d9-4dbd-8e32-ad04bf8b6cec

Comm: tcp://127.0.0.1:55489	Workers: 4
Dashboard: http://127.0.0.1:8787/status	Total threads: 12
Started: Just now	Total memory: 16.00 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:55504	Total threads: 3
Dashboard: http://127.0.0.1:55505/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:55492
Local directory: /Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-uyorb34d

Worker: 1

Comm: tcp://127.0.0.1:55511	Total threads: 3
Dashboard: http://127.0.0.1:55514/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:55494
Local directory: /Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-5r82wbzs

Worker: 2

Comm: tcp://127.0.0.1:55510	Total threads: 3
Dashboard: http://127.0.0.1:55512/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:55495
Local directory: /Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-nrg7jfnz

Worker: 3

Comm: tcp://127.0.0.1:55507	Total threads: 3
Dashboard: http://127.0.0.1:55508/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:55493
Local directory: /Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-mnh6sj2t

Access the data#

We have an intake catalog we can read in!

data_catalog = intake.open_esm_datastore("catalogs/arise-catalog.json")

data_catalog.search(variable='TREFHT').keys()

['month_1.SSP245-TSMLT-GAUSS-DEFAULT.1',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.2',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.3',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.4',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.5',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.6',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.7',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.8',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.9',
 'month_1.SSP245-TSMLT-GAUSS-DEFAULT.10']

Load the Data Using Dask#

dsets = data_catalog.search(variable='TREFHT').to_dataset_dict()

--> The keys in the returned dictionary of datasets are constructed as follows:
	'frequency.experiment.member_id'

100.00% [10/10 00:01<00:00]

sorted_keys = sorted(dsets.keys(), key=lambda x: (len(x), x))

Investigate our Dataset#

Let’s investigate our dataset!

Plot Using Matplotlib#

We can start with a single time step

for key in sorted_keys:
    ds = dsets[key].isel(time=0)
    ds.TREFHT.plot()
    plt.title(f"{ds.time.values} \n {ds.case}")
    plt.show()
    plt.close()

And a single point

for key in sorted_keys:
    ds = dsets[key].sel(lat=41.8781,
                        lon=-87.6298,
                        method='nearest')
    ds.TREFHT.plot()
    plt.title(f"Chicago, IL \n {ds.case}")
    plt.show()
    plt.close()

Plot Using hvPlot#

Let’s use an interactive plotting library!